# Containing a spread through sequential learning: to exploit or to explore?

arXiv: https://arxiv.org/abs/2303.00141 · HTML: https://mlsanity.com/paper/2303.00141/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 75 references that are in the map.
- First posted on arXiv 1 Mar 2023.

## Builds on (most cited first, 14 of 14)

- [Exploration by Random Network Distillation](https://mlsanity.com/paper/1810.12894/) (2018)
- [Unifying Count-Based Exploration and Intrinsic Motivation](https://mlsanity.com/paper/1606.01868/) (2016)
- [A Review of Stochastic Block Models and Extensions for Graph Clustering](https://mlsanity.com/paper/1903.00114/) (2019)
- [Scale-free networks are rare](https://mlsanity.com/paper/1801.03400/) (2018)
- [Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks](https://mlsanity.com/paper/2010.05313/) (2020)
- [Bayes-Ball: The Rational Pastime (for Determining Irrelevance and Requisite Information in Belief Networks and Influence Diagrams)](https://mlsanity.com/paper/1301.7412/) (2013)
- [Modeling, state estimation, and optimal control for the US COVID-19 outbreak](https://mlsanity.com/paper/2004.06291/) (2020)
- [SIMLR: Machine Learning inside the SIR model for COVID-19 Forecasting](https://mlsanity.com/paper/2106.01590/) (2021)
- [Analysis of the COVID-19 pandemic by SIR model and machine learning technics for forecasting](https://mlsanity.com/paper/2004.01574/) (2020)
- [A framework for optimizing COVID-19 testing policy using a Multi Armed Bandit approach](https://mlsanity.com/paper/2007.14805/) (2020)
- [Optimal adaptive testing for epidemic control: combining molecular and serology tests](https://mlsanity.com/paper/2101.00773/) (2021)
- [Comparative prediction of confirmed cases with COVID-19 pandemic by machine learning, deterministic and stochastic SIR models](https://mlsanity.com/paper/2004.13489/) (2020)
- [Crackovid: Optimizing Group Testing](https://mlsanity.com/paper/2005.06413/) (2020)
- [A Partially Observable MDP Approach for Sequential Testing for Infectious Diseases such as COVID-19](https://mlsanity.com/paper/2007.13023/) (2020)

## Built on by (most cited first, 1 of 1)

- [Adaptive Requesting in Decentralized Edge Networks via Non-Stationary Bandits](https://mlsanity.com/paper/2601.08760/) (2026)

## Abstract (arXiv)

The spread of an undesirable contact process, such as an infectious disease (e.g. COVID-19), is contained through testing and isolation of infected nodes. The temporal and spatial evolution of the process (along with containment through isolation) render such detection as fundamentally different from active search detection strategies. In this work, through an active learning approach, we design testing and isolation strategies to contain the spread and minimize the cumulative infections under a given test budget. We prove that the objective can be optimized, with performance guarantees, by greedily selecting the nodes to test. We further design reward-based methodologies that effectively minimize an upper bound on the cumulative infections and are computationally more tractable in large networks. These policies, however, need knowledge about the nodes' infection probabilities which are dynamically changing and have to be learned by sequential testing. We develop a message-passing framework for this purpose and, building on that, show novel tradeoffs between exploitation of knowledge through reward-based heuristics and exploration of the unknown through a carefully designed probabilistic testing. The tradeoffs are fundamentally distinct from the classical counterparts under active search or multi-armed bandit problems (MABs). We provably show the necessity of exploration in a stylized network and show through simulations that exploration can outperform exploitation in various synthetic and real-data networks depending on the parameters of the network and the spread.